Generating Link Suggestions

Overview

RankVectors uses AI to analyze your content and generate relevant internal linking suggestions. Suggestions are based on:

• Semantic similarity: Content meaning and context
• Relevance score: How related pages are
• Link opportunities: Missing or underutilized links
• Page authority: Importance and popularity

Generate Suggestions

For a Single Page

const suggestions = await client.suggestions.generate(projectId, {
  sourcePageUrl: 'https://example.com/blog/post-1',
  limit: 10
})

for (const suggestion of suggestions) {
  console.log(`Link from "${suggestion.sourceTitle}" to "${suggestion.targetTitle}"`)
  console.log(`Relevance: ${suggestion.relevanceScore}`)
  console.log(`Context: ${suggestion.context}`)
}

For Multiple Pages

Generate suggestions for multiple source pages:

const sourcePages = [
  'https://example.com/page1',
  'https://example.com/page2',
  'https://example.com/page3'
]

for (const sourceUrl of sourcePages) {
  const suggestions = await client.suggestions.generate(projectId, {
    sourcePageUrl: sourceUrl,
    limit: 5
  })
  
  // Process suggestions...
}

Suggestion Parameters

Limit Results

const suggestions = await client.suggestions.generate(projectId, {
  sourcePageUrl: 'https://example.com/page',
  limit: 20 // Max number of suggestions
})

Filter by Relevance

const suggestions = await client.suggestions.generate(projectId, {
  sourcePageUrl: 'https://example.com/page',
  minRelevanceScore: 0.8 // Only suggestions with 80%+ relevance
})

Target Specific Pages

const suggestions = await client.suggestions.generate(projectId, {
  sourcePageUrl: 'https://example.com/page',
  targetPageUrls: [
    'https://example.com/target1',
    'https://example.com/target2'
  ]
})

Understanding Suggestions

Each suggestion includes:

• Source Page: The page that should link out
• Target Page: The page to link to
• Anchor Text: Recommended link text
• Context: Where in the content to add the link
• Relevance Score: 0-1 score of how relevant the link is
• Potential Impact: Estimated SEO/value improvement

{
  id: 'sugg_123',
  sourcePageUrl: 'https://example.com/blog/seo-tips',
  targetPageUrl: 'https://example.com/blog/keyword-research',
  anchorText: 'keyword research',
  context: 'Learn about keyword research techniques...',
  relevanceScore: 0.92,
  potentialImpact: 'high',
  position: {
    lineNumber: 45,
    offset: 120
  }
}

Reviewing Suggestions

List All Suggestions

const suggestions = await client.suggestions.list(projectId, {
  status: 'pending', // pending, approved, rejected, implemented
  limit: 50,
  offset: 0
})

Get Specific Suggestion

const suggestion = await client.suggestions.get(projectId, suggestionId)

console.log(suggestion.context) // See suggested context
console.log(suggestion.relevanceScore) // Check relevance

Managing Suggestions

Approve Suggestion

await client.suggestions.update(projectId, suggestionId, {
  status: 'approved'
})

Reject Suggestion

await client.suggestions.update(projectId, suggestionId, {
  status: 'rejected',
  reason: 'Not relevant to content'
})

Mark as Implemented

await client.suggestions.update(projectId, suggestionId, {
  status: 'implemented'
})

Best Practices

1. Start with high-relevance suggestions: Focus on 0.8+ scores
2. Consider context: Ensure links make sense in the content flow
3. Diversify anchor text: Use natural, varied anchor text
4. Track implementation: Mark suggestions as implemented to track progress
5. Monitor results: Use analytics to see which links perform best